In a survey when scientists were asked about reproducibility in science, more than 70% of them claimed to have failed to reproduce another scientist’s results, and more than 50% have failed to reproduce their own results.
When we talk about data in science, reproducibility and replicability are among the major keys to what is known as data integrity.
There are roughly three major topics that can be derived from this concept. They include:
- Data replicability
- Data Reproducibility, and;
- Research reproducibility
Although they all sound similar, they are quite different. But, our focus in this article is on reproducibility.
Before we delve into the importance of reproducibility, we must have an idea of what it is all about. In defining reproducibility research, it has been noted as the ability to run data analysis and attain the same result as another person.
While data replication and reproduction are associated with the generation of data, research reproducibility is strictly repeating all of the analyses.
Why We Care About Reproducibility In Science?
Some of the reasons why Reproducibility is important in science include:
It is evidence of accuracy
A very obvious reason for having to undergo the process of reproducing research and repeating the analysis is for confirmation. It is done to confirm if the original results are actually correct
When a totally different scientist reproduces your research and arrives at the same conclusion, it is clear that the conclusion is actually correct.
This is the same with negative results. Here, reproducing the analysis can aid in confirming if they are actually erroneous or simply insignificant statistically.
It brings about newer observations
Most times, there are diverse ways of analyzing the same results. This means that there is a likely prospect in reaching diverse conclusions via different analyses. In turn, different conclusions are taken to be interesting.
This is because claims and findings can not only be created; new observations will also be made. When new light is shed on results and even considered in other alternative ways, it leads to discoveries.
The higher complexity of data analyses
Currently, the difficulty of data analysis has attained an all-time high. The data sets are now bigger, and computations have become much more exquisite.
What happens is that all of these will require Reproducibility in a bid to decrease error and bias when humans are joined to the data analysis process. This is quite inevitable as scientists are still humans and prone to mistakes.
When research is reproducible, it will require that the raw data is available and aid others make complete analyses that are not as biased as the previous ones.
To Wrap It Up
Reproducibility in science enables the vital aspects of data analysis not to be missed. Sometimes, data analyses get lost in some superficial summaries that are only written to convince readers of their significance.
However, when research in science is reproducible, every step is included so that people can understand how and why the conclusions were drawn. This will help in giving much more context to the analysis.
It will practically allow others to follow due process and arrive at their own conclusions as well.